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Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
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Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 (Paperback, 1st ed. 2016)
Loot Price: R4,979
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Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 (Paperback, 1st ed. 2016)
Series: Advances in Intelligent Systems and Computing, 428
Expected to ship within 18 - 22 working days
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This book contains the articles from the international conference
11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at
Rice University in Houston, Texas, 6-8 January 2016. WSOM is a
biennial international conference series starting with WSOM'97 in
Helsinki, Finland, under the guidance and direction of Professor
Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings
together the state-of-the-art theory and applications in
Competitive Learning Neural Networks: SOMs, LVQs and related
paradigms of unsupervised and supervised vector quantization.The
current proceedings present the expert body of knowledge of 93
authors from 15 countries in 31 peer reviewed contributions. It
includes papers and abstracts from the WSOM 2016 invited speakers
representing leading researchers in the theory and real-world
applications of Self-Organizing Maps and Learning Vector
Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon
Sorbonne, France), Professor Pablo Estevez (University of Chile and
Millennium Instituteof Astrophysics, Chile), and Professor Risto
Miikkulainen (University of Texas at Austin, USA). The book
comprises a diverse set of theoretical works on Self-Organizing
Maps, Neural Gas, Learning Vector Quantization and related topics,
and an excellent variety of applications to data visualization,
clustering, classification, language processing, robotic control,
planning, and to the analysis of astronomical data, brain images,
clinical data, time series, and agricultural data.
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